{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.8\n",
    "session = tf.Session(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.logging.set_verbosity(tf.logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_samples(size=1, variance=0.03,randomness=True):\n",
    "    \"\"\"\n",
    "    Generates samples as described in the paper\n",
    "    \"\"\"\n",
    "    alpha = 4\n",
    "    beta = 13\n",
    "    if  randomness:\n",
    "        # Heteroscedastic noise\n",
    "        w = np.random.normal(0, variance, size=size)\n",
    "        x1 = np.linspace(0, 0.6, 0.6 * size)\n",
    "        x2 = np.linspace(0.8, 1.0, 0.4 * size)\n",
    "        \n",
    "        #x1 = np.random.uniform(low=0, high=0.6, size=size)\n",
    "        #x2 = np.random.uniform(low=0.8, high=1.0, size=size)\n",
    "    \n",
    "        x = np.append(x1, x2)\n",
    "    else:\n",
    "        w = np.zeros(size*2)\n",
    "        x = np.linspace(0, 1, size * 2)\n",
    "    \n",
    "    y = x + np.sin(alpha * (x + w)) + np.sin(beta * (x + w)) + w\n",
    "    \n",
    "    return x, y\n",
    "\n",
    "def generate_samples_heteroscedastic(size=1, variance=0.03,randomness=True):\n",
    "    alpha = 4\n",
    "    beta = 13\n",
    "    if  randomness:\n",
    "        # Heteroscedastic noise\n",
    "        w1 = np.random.normal(0, variance, size=int(0.6*size))\n",
    "        x1 = np.linspace(0, 0.6, 0.6 * size)\n",
    "        \n",
    "        w2 = np.random.normal(0, variance*0, size=int(0.4*size))\n",
    "        x2 = np.linspace(0.8, 1.0, 0.4 * size)\n",
    "    \n",
    "        x = np.append(x1, x2)\n",
    "        w = np.append(w1, w2)\n",
    "    else:\n",
    "        w = np.zeros(size*2)\n",
    "        x = np.linspace(0, 1, size * 2)\n",
    "    \n",
    "    y = x + np.sin(alpha * (x + w)) + np.sin(beta * (x + w)) + w\n",
    "    \n",
    "    return x, y, w\n",
    "\n",
    "def generate_nonlinear():\n",
    "    \"\"\" Example from Risk vs Uncertainty paper\"\"\"\n",
    "    x = np.array([-1, -1, 0.01, 0.01, 1, 1])\n",
    "    y = np.array([-1, 1, -1, 1, -1, 1])\n",
    "    \n",
    "    return x, y\n",
    "\n",
    "\n",
    "def generate_linear():\n",
    "    x = np.array(range(1000000))\n",
    "    y = np.array(range(1000000))\n",
    "    return x, y\n",
    "\n",
    "def generate_sin():\n",
    "    x = np.array(np.arange(-2, 2, 0.0001))\n",
    "    y = np.sin(x)\n",
    "    \n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fully-Connected with Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-48c178ea45ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEstimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcn_dropout_network\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;31m#x, y = generate_samples(50)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
     ]
    }
   ],
   "source": [
    "n_passes = 30\n",
    "dropout_rate = 0.5\n",
    "\n",
    "def fcn_dropout_network(features, labels, mode):\n",
    "    with tf.device(\"/gpu:0\"):\n",
    "        training = True\n",
    "        \n",
    "        input_layer = tf.feature_column.input_layer(features, tf.feature_column.numeric_column(\"x\"))\n",
    "\n",
    "        fc1 = tf.layers.dense(inputs=input_layer, units=50, activation=tf.nn.relu)\n",
    "        fc1 = tf.layers.dropout(fc1, dropout_rate, training=True)\n",
    "        \n",
    "        fc2 = tf.layers.dense(inputs=fc1, units=50, activation=tf.nn.relu)\n",
    "        fc2 = tf.layers.dropout(fc2, dropout_rate, training=True)\n",
    "\n",
    "        output_layer = tf.layers.dense(inputs=fc2, units=1)\n",
    "        predictions = tf.reshape(output_layer, [-1])\n",
    "        \n",
    "        if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "            return tf.estimator.EstimatorSpec(\n",
    "                mode=mode, predictions=predictions)\n",
    "\n",
    "        average_loss =  tf.losses.mean_squared_error(labels, predictions)\n",
    "        average_loss = tf.identity(average_loss, name=\"average_loss\")\n",
    "\n",
    "        batch_size = tf.shape(labels)[0]\n",
    "        total_loss = tf.to_float(batch_size) * average_loss\n",
    "\n",
    "        if mode == tf.estimator.ModeKeys.TRAIN:\n",
    "            optimizer = tf.train.AdamOptimizer(learning_rate=0.01)\n",
    "            train_op = optimizer.minimize(\n",
    "                loss=total_loss, global_step=tf.train.get_global_step())\n",
    "\n",
    "            return tf.estimator.EstimatorSpec(\n",
    "                mode=mode, loss=average_loss, train_op=train_op)\n",
    "\n",
    "\n",
    "model = tf.estimator.Estimator(model_fn=fcn_dropout_network)\n",
    "\n",
    "#x, y = generate_samples(50)\n",
    "\n",
    "x, y = generate_nonlinear()\n",
    "\n",
    "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"x\": x}, y=y, shuffle=True, num_epochs=10**6)\n",
    "# steps total\n",
    "model.train(input_fn=train_input_fn, steps=10000)\n",
    "\n",
    "#pred_x = np.arange(-0.3, 1.2, 0.01)\n",
    "pred_x = np.arange(-3, 3, 0.01)\n",
    "pred_x = np.array([[e] * n_passes for e in pred_x]).flatten()\n",
    "\n",
    "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"x\": pred_x}, shuffle=False)\n",
    "predictions = model.predict(input_fn=predict_input_fn)\n",
    "pred_y = np.array([p for p in predictions])\n",
    "\n",
    "# Recalculate Means and Variances from extended pred_x and pred_y\n",
    "pred_x = pred_x.reshape(-1, n_passes).mean(axis=1)\n",
    "pred_y_old = pred_y\n",
    "pred_y = pred_y.reshape(-1, n_passes).mean(axis=1)\n",
    "var = pred_y_old.reshape(-1, n_passes).std(axis=1)\n",
    "\n",
    "#f, ax = plt.subplots(1, )\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(x, y, label=\"training samples\")\n",
    "plt.scatter(pred_x, pred_y, color=\"red\", label=\"prediction\")\n",
    "#plt.plot(x_true, y_true, color=\"black\", label=\"true\")\n",
    "plt.fill_between(pred_x, pred_y - var, pred_y + var, alpha=0.5)\n",
    "plt.fill_between(pred_x, 0, var, alpha=0.5, label=\"variance\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fully-Connected with Bootstrap\n",
    "\n",
    "Implementation can either be done with $K$ separated networks or a shared network with $K$ heads. For each sample there is a $K$ Bit vector $m_i$ denoting whether head $k$ is trained with this sample. $m_i$ is drawn from the masking distribution $M$. It can be Bernoulli(0.5), Poisson \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import bernoulli\n",
    "import numpy.ma as ma\n",
    "\n",
    "#n_samples = 50\n",
    "#x, y = generate_samples(n_samples)\n",
    "\n",
    "def evaluate_bootstrap(x, y, pred_range, n_heads=5, training_epochs=10000, plotting=True):\n",
    "    \"\"\"\n",
    "    \n",
    "    Keyword arguments:\n",
    "    x -- samples x-coordinates\n",
    "    y -- samples y-coordinates\n",
    "    pred_range -- range for predictions, needs special shape\n",
    "    n_head -- number of heads\n",
    "    plotting -- indicate if output is plotted\n",
    "    \"\"\"\n",
    "\n",
    "    assert len(x) == len(y)\n",
    "\n",
    "    #assert len(x) == n_samples\n",
    "    #assert len(y) == n_samples\n",
    "\n",
    "    x = x.astype(np.float32)\n",
    "    y = y.astype(np.float32)\n",
    "    #n_heads = 10\n",
    "\n",
    "    rv = bernoulli(0.5)\n",
    "    # For every data point, associated heads\n",
    "    # mask = rv.rvs(size=(n_samples, K)) \n",
    "\n",
    "    # For every mask the associated data points\n",
    "    mask = rv.rvs(size=(n_heads, len(x)))\n",
    "\n",
    "    # TODO add initializer\n",
    "    x_data = tf.placeholder(tf.float32, [None, 1])\n",
    "    y_data = tf.placeholder(tf.float32, [None, 1])\n",
    "\n",
    "    def bootstrap_network(x, n_heads):\n",
    "        with tf.device(\"/gpu:0\"):\n",
    "\n",
    "            #x = tf.reshape(x, [-1, 1])\n",
    "            fc1 = tf.layers.dense(inputs=x, units=50, activation=tf.nn.relu)\n",
    "            # Place second layer in shared network \n",
    "            #fc2 = tf.layers.dense(inputs=fc1, units=20, activation=tf.nn.relu)\n",
    "\n",
    "            heads = []\n",
    "            for i in range(n_heads):\n",
    "                fc2 = tf.layers.dense(inputs=fc1, units=50, activation=tf.nn.relu)\n",
    "                heads.append(tf.layers.dense(inputs=fc2, units=1))\n",
    "\n",
    "            return heads\n",
    "\n",
    "    heads = bootstrap_network(x_data, n_heads)\n",
    "\n",
    "    learning_rate = 0.01\n",
    "    #training_epochs = 10000\n",
    "    display_step = 2000\n",
    "\n",
    "    # optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "\n",
    "    loss_per_head = []\n",
    "    train_per_head = []\n",
    "    for head in heads:\n",
    "        loss = tf.losses.mean_squared_error(y_data, head)\n",
    "        #loss = tf.reduce_mean(tf.square(head - y_data))\n",
    "        loss_per_head.append(loss)\n",
    "        train_per_head.append(optimizer.minimize(loss))\n",
    "\n",
    "    init = tf.global_variables_initializer()                                  \n",
    "    sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "    sess.run(init)\n",
    "\n",
    "    for epoch in range(training_epochs):\n",
    "\n",
    "        for i, t in enumerate(train_per_head):\n",
    "            masked_x = ma.masked_array(x, mask[i]).compressed().reshape([-1, 1])\n",
    "            masked_y = ma.masked_array(y, mask[i]).compressed().reshape([-1, 1]) \n",
    "            sess.run(t, feed_dict={x_data: masked_x, y_data: masked_y})\n",
    "\n",
    "        if (epoch % display_step == 0):\n",
    "            print(\"Epoch {}\".format(epoch))\n",
    "            for i, loss in enumerate(loss_per_head):\n",
    "                curLoss = sess.run(loss, feed_dict={x_data: x.reshape([-1, 1]),\n",
    "                                                    y_data: y.reshape([-1, 1])})\n",
    "                print(\"Head {}: Loss {}\".format(i, curLoss))\n",
    "            print(\"================\")\n",
    "\n",
    "    print(\"Training done\")\n",
    "    \n",
    "    if plotting == True:\n",
    "        f, axs = plt.subplots(2, 1, sharey=True, figsize=(20,20))\n",
    "        \n",
    "        # TODO: Check shape\n",
    "        pred_x = pred_range.reshape([-1, 1])\n",
    "        #pred_x = np.arange(-3, 3.0, 0.01).reshape([-1, 1])\n",
    "        #pred_x = np.linspace(-0.2, 1.2, 100).reshape([-1, 1])\n",
    "        pred_ys = []\n",
    "        plt.figure(figsize=(20,10))\n",
    "        for i, head in enumerate(heads):\n",
    "            pred_y = sess.run(head, feed_dict={x_data: pred_x})\n",
    "            pred_ys.append(pred_y)\n",
    "            axs[0].plot(pred_x, pred_y, label=\"Head \" + str(i))\n",
    "        axs[0].scatter(x, y, label=\"training samples\")\n",
    "\n",
    "        y_squeezed = np.squeeze(pred_ys, axis=2).transpose()\n",
    "        y_mean = np.mean(y_squeezed, axis=1)\n",
    "        y_var = np.std(y_squeezed, axis=1)\n",
    "        axs[0].legend()\n",
    "        axs[0].fill_between(np.squeeze(pred_x, axis=1), y_mean - y_var, y_mean + y_var, alpha=0.3)\n",
    "        \n",
    "        lower = axs[0].get_ylim()[0]\n",
    "        axs[0].fill_between(np.squeeze(pred_x, axis=1), lower, lower + y_var, alpha=0.1)\n",
    "\n",
    "        _ = axs[0].set_title(\"Different head approximations and their Standard Deviation\")\n",
    "        \n",
    "        #axs[1].fill_between(np.squeeze(pred_x, axis=1), 0, y_var, alpha=0.5)\n",
    "\n",
    "        axs[1].scatter(x, y)\n",
    "        axs[1].plot(pred_x, y_mean, linewidth=1, alpha=1)\n",
    "        axs[1].fill_between(np.squeeze(pred_x, axis=1), y_mean - y_var, y_mean + y_var, alpha=0.1, color=\"blue\")\n",
    "        _ = axs[1].set_title(\"Mean and Standard Deviation\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Head 0: Loss 1.00296068192\n",
      "Head 1: Loss 1.03328800201\n",
      "Head 2: Loss 1.0793864727\n",
      "Head 3: Loss 1.05469048023\n",
      "Head 4: Loss 1.0037842989\n",
      "================\n",
      "Epoch 2000\n",
      "Head 0: Loss 1.00458395481\n",
      "Head 1: Loss 1.40892505646\n",
      "Head 2: Loss 2.93875813484\n",
      "Head 3: Loss 1.66723501682\n",
      "Head 4: Loss 1.66663706303\n",
      "================\n",
      "Epoch 4000\n",
      "Head 0: Loss 1.00421857834\n",
      "Head 1: Loss 1.41326797009\n",
      "Head 2: Loss 2.54089140892\n",
      "Head 3: Loss 1.66765975952\n",
      "Head 4: Loss 1.66646182537\n",
      "================\n",
      "Epoch 6000\n",
      "Head 0: Loss 1.0052036047\n",
      "Head 1: Loss 1.50690233707\n",
      "Head 2: Loss 2.12044405937\n",
      "Head 3: Loss 1.66530573368\n",
      "Head 4: Loss 1.66582000256\n",
      "================\n",
      "Training done\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIMAAARuCAYAAABA0ferAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEAAAAECNCIMAAAAAauT/B1j6zzG9vAE1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e3ec47910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e3ea4e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = generate_nonlinear()\n",
    "pred_range =np.arange(-3, 3.0, 0.01) # evaluation range\n",
    "evaluate_bootstrap(x, y, pred_range, 5, training_epochs=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/disk/users/hutmache/.Envs/ros_e2e/lib/python2.7/site-packages/ipykernel_launcher.py:9: DeprecationWarning: object of type <type 'float'> cannot be safely interpreted as an integer.\n",
      "  if __name__ == '__main__':\n",
      "/disk/users/hutmache/.Envs/ros_e2e/lib/python2.7/site-packages/ipykernel_launcher.py:10: DeprecationWarning: object of type <type 'float'> cannot be safely interpreted as an integer.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Head 0: Loss 0.989405870438\n",
      "Head 1: Loss 1.01352262497\n",
      "Head 2: Loss 1.10206615925\n",
      "Head 3: Loss 1.06674528122\n",
      "Head 4: Loss 1.02920687199\n",
      "================\n",
      "Epoch 2000\n",
      "Head 0: Loss 0.0775962993503\n",
      "Head 1: Loss 0.0412382036448\n",
      "Head 2: Loss 0.0727954953909\n",
      "Head 3: Loss 0.0499521270394\n",
      "Head 4: Loss 0.0539410784841\n",
      "================\n",
      "Epoch 4000\n",
      "Head 0: Loss 0.0701469630003\n",
      "Head 1: Loss 0.0505595952272\n",
      "Head 2: Loss 0.0643258318305\n",
      "Head 3: Loss 0.047948975116\n",
      "Head 4: Loss 0.0482284538448\n",
      "================\n",
      "Epoch 6000\n",
      "Head 0: Loss 0.0802385509014\n",
      "Head 1: Loss 0.0549997538328\n",
      "Head 2: Loss 0.0678777247667\n",
      "Head 3: Loss 0.0510334521532\n",
      "Head 4: Loss 0.0616628825665\n",
      "================\n",
      "Training done\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIoAAARuCAYAAAC8xNxhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e3f70c490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e3f45fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = 50\n",
    "x, y = generate_samples(n_samples)\n",
    "pred_range =np.arange(-0.2, 1.2, 0.01) # evaluation range\n",
    "evaluate_bootstrap(x, y, pred_range, 5, training_epochs=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAARuCAYAAAD6a8LmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAW2oOIgim5gPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e84709e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the samples for the individual heads\n",
    "f, axs = plt.subplots(n_heads, 1, sharey=True, figsize=(10,20))\n",
    "for i in range(n_heads):\n",
    "    ax = axs[i]\n",
    "    masked_x = ma.masked_array(x, mask[i]).compressed().reshape([-1, 1])\n",
    "    masked_y = ma.masked_array(y, mask[i]).compressed().reshape([-1, 1])\n",
    "    ax.set_title(str(i))\n",
    "    ax.scatter(masked_x, masked_y, label=str(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining aleatoric and epistemic uncertainty\n",
    "\n",
    "- Learn to output $\\sigma$\n",
    "- Define new loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/disk/users/hutmache/.Envs/ros_e2e/lib/python2.7/site-packages/ipykernel_launcher.py:31: DeprecationWarning: object of type <type 'float'> cannot be safely interpreted as an integer.\n",
      "/disk/users/hutmache/.Envs/ros_e2e/lib/python2.7/site-packages/ipykernel_launcher.py:34: DeprecationWarning: object of type <type 'float'> cannot be safely interpreted as an integer.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0\n",
      "Loss: [  2.07166478e-01   6.73576966e-02   2.80653546e-03   4.41391647e-01\n",
      "   8.50896657e-01   5.33746421e-01   1.19857883e+00   1.36883818e-02\n",
      "   9.09053028e-01   1.33329630e+00   1.29916036e+00   1.05618703e+00\n",
      "   1.36655092e+00   1.37045729e+00   1.39264965e+00   1.08125389e+00\n",
      "   1.36773419e+00   1.42457902e+00   1.22959435e+00   8.96091580e-01\n",
      "   1.25807786e+00   1.04795420e+00   5.52114129e-01   3.97085309e-01\n",
      "   1.04452014e+00   2.62191504e-01   2.27865681e-01   3.52064401e-01\n",
      "   7.01718569e-01   1.97946802e-01   2.90593684e-01   1.15437903e-01\n",
      "   5.42759076e-02   1.17404819e-01   9.64115039e-02   1.15319148e-01\n",
      "   7.79482871e-02   1.50113493e-01   1.28682375e-01   2.45486587e-01\n",
      "   1.45824730e-01   4.46417242e-01   5.44036627e-01   7.34455884e-01\n",
      "   5.12724340e-01   9.18620646e-01   1.09229207e+00   2.27886534e+00\n",
      "   6.62845373e-01   2.53424048e+00   2.08010149e+00   1.65486813e+00\n",
      "   1.66089368e+00   1.92152894e+00   2.55880809e+00   2.59024954e+00\n",
      "   2.11086559e+00   2.51899457e+00   3.15898895e+00  -9.96382087e-02\n",
      "  -2.11450681e-02  -1.36171188e-02   6.77090045e-03  -2.64223032e-02\n",
      "  -6.41224533e-02   1.22966416e-01  -2.30691917e-02  -1.89617798e-02\n",
      "  -5.41713573e-02   8.51025432e-02   2.79706232e-02   6.78771734e-03\n",
      "   6.27264008e-02   3.93666700e-02   2.24702600e-02  -1.65240824e-01\n",
      "   7.41936266e-03  -3.40179726e-03   1.62956268e-02   6.95704948e-03\n",
      "  -2.15455703e-02   1.98483244e-02  -1.99239347e-02  -4.84382510e-02\n",
      "   3.39750499e-02  -4.37555127e-02  -8.41987506e-02   2.22931430e-03\n",
      "   3.32343951e-02   3.35920155e-02  -2.27254927e-02   2.54053883e-02\n",
      "   7.45816976e-02   3.16318274e-02   1.22953236e-01   1.92862391e-01\n",
      "   1.43734962e-01   3.20236266e-01   7.35663772e-01]\n",
      "================\n",
      "Epoch 2000\n",
      "Loss: [ 2.16532779  0.51967293  1.42816567 -0.41886988 -0.52264237 -0.4825992\n",
      " -0.2956084   0.33355483 -0.39074266 -0.26657531  0.03995451 -0.23421089\n",
      "  0.16801536 -0.23085833 -0.1303114  -0.31227872 -0.10425889 -0.07603928\n",
      " -0.19879135 -0.35179985 -0.41428998 -0.25198999 -0.49331188 -0.44251734\n",
      " -0.29419518 -0.37889019 -0.08293244 -0.39342153 -0.43529335 -0.30212095\n",
      " -0.4365145   0.02697247  0.34975892 -0.16623706  0.2037057   0.32087642\n",
      "  0.17498514 -0.15979505  0.40088126 -0.21361434 -0.16347092 -0.38180876\n",
      " -0.43198788 -0.42718673 -0.49181977 -0.4856171  -0.28776014  0.68435168\n",
      " -0.42004815  0.88888395  0.73211336  0.53159612  0.06431907  0.28165734\n",
      "  1.34124899  1.13347125  0.66408169  0.95026058  1.33358729 -0.42606348\n",
      " -0.5137682  -0.36618474 -0.45026046 -0.23654005 -0.30468422 -0.20912281\n",
      " -0.31741095 -0.1748172  -0.44561857  0.16039416  0.31970024  0.08973458\n",
      " -0.11940107 -0.16800728 -0.22939557 -0.03392035 -0.29189909 -0.17782232\n",
      " -0.13813916  0.16939503 -0.49568903 -0.68349379 -0.61363131 -0.34131613\n",
      " -0.71360701 -0.62608719 -0.61114085 -0.48344684 -0.62218314 -0.54583067\n",
      " -0.30879027 -0.40381688  0.10798228 -0.27625388 -0.02857506 -0.32642743\n",
      "  0.53003967  0.481619   -0.24962166]\n",
      "================\n",
      "Epoch 4000\n",
      "Loss: [ 3.63381505  0.10209614  1.07058191 -0.37347418 -0.32496321 -0.35198942\n",
      " -0.4329356   0.62051898 -0.28147331  0.00818878  0.03805661 -0.44167751\n",
      " -0.25939161  0.13854632 -0.50316471 -0.29145324  0.04492068  0.06499165\n",
      " -0.14126354 -0.5002383   0.37178817 -0.46568176 -0.54331452 -0.44211784\n",
      " -0.19093451  0.38340819 -0.23469456 -0.1065383  -0.35098726 -0.50234747\n",
      " -0.24649006 -0.14368388  0.90830946 -0.16685915  0.19591948  0.34472996\n",
      "  0.25306177  0.51664436  0.17130774 -0.43760455 -0.17155874 -0.48322788\n",
      " -0.40319866 -0.49987292 -0.336198   -0.50007111 -0.294034    0.07898581\n",
      " -0.45839009  0.86337388  1.25467718  0.91193545  0.42453912  0.42677256\n",
      "  0.6411674   1.31388831  0.51741958  1.3099618   0.71229947 -0.48418331\n",
      " -0.36202317 -0.50434637 -0.50771368 -0.28102067 -0.22671336 -0.20788494\n",
      " -0.54251206 -0.62666523 -0.22297713 -0.20031273 -0.1610468  -0.17149687\n",
      " -0.3513132  -0.19333127 -0.25226602 -0.13909203 -0.21188089 -0.51989812\n",
      " -0.63026583 -0.59846848 -0.67503238 -0.70661038 -0.66185206 -0.984532\n",
      " -0.68055588 -0.77404284 -0.70354569 -0.70343924 -0.7079764  -0.63805938\n",
      " -0.62152749 -0.45811799 -0.36369118 -0.33646405  0.01512951 -0.26043421\n",
      " -0.22375411 -0.272515    0.71539384]\n",
      "================\n",
      "Epoch 6000\n",
      "Loss: [ 2.66758513  0.08709931  0.44833022 -0.22470486 -0.1609256  -0.2989468\n",
      " -0.28696242  0.6790452  -0.42912403 -0.07123148 -0.27822155 -0.43823019\n",
      " -0.31112605 -0.09936917 -0.20866019 -0.43953288 -0.09102201  0.16981634\n",
      " -0.40332752 -0.45742291  0.47947547 -0.46148583 -0.45572644 -0.29249978\n",
      " -0.44042504  0.16261315 -0.34341499 -0.45491713 -0.47145596 -0.14477727\n",
      " -0.28066206  0.28622967  0.42876512 -0.19106746  0.11449617  0.68960905\n",
      " -0.36383402  0.10349688  0.37843287 -0.09766781 -0.23242256 -0.43655375\n",
      " -0.45069203 -0.38265288 -0.44616121 -0.34037068 -0.31124321  0.38854337\n",
      " -0.37328625  0.59652996  0.47750434 -0.20801088  0.38369149  0.0640108\n",
      "  3.537992    0.51550692  0.45332617  1.13347542  1.24615967 -0.8377952\n",
      " -0.82210207 -0.93255883 -0.77722406 -0.63650024 -0.48036158 -0.50197136\n",
      " -0.44050181 -0.43332502 -0.27894634 -0.16466016 -0.32277751 -0.41275781\n",
      " -0.4510271  -0.40681845 -0.14505792 -0.41245836 -0.52043051 -0.66116995\n",
      " -0.70230848 -0.51062179 -0.23098406 -0.83164757 -0.7837925  -0.9016974\n",
      " -1.00068474 -0.91190577 -0.80014998 -0.84373701 -1.01317692 -0.78089869\n",
      " -0.66876179 -0.87848788 -0.56058496 -0.40201077 -0.26443124  0.0716188\n",
      " -0.14200616  0.78822899  0.45728457]\n",
      "================\n",
      "Epoch 8000\n",
      "Loss: [ 2.77496147 -0.27381629  1.41972303 -0.38251117 -0.45435894 -0.50834394\n",
      " -0.11956269  0.7825309  -0.35514048 -0.26672924 -0.37059382 -0.0625006\n",
      "  0.44908172 -0.1607528  -0.15100366  0.18484503 -0.44172126  1.13609564\n",
      "  0.408867   -0.53620613 -0.40771213 -0.3358227  -0.26517165 -0.18519454\n",
      " -0.21460852 -0.25529882 -0.4478229  -0.47346276 -0.1707181  -0.05202678\n",
      " -0.29019094 -0.31933427  0.18818849  0.261574   -0.46185839 -0.21021062\n",
      "  0.18540514  0.47238454 -0.26633605 -0.43330655  0.18698213 -0.35286736\n",
      " -0.51424515 -0.47149616 -0.42543617 -0.39767504 -0.27527255  0.3895368\n",
      " -0.33899018  1.46531248  1.50941706  1.73820066  1.09563673  0.97095186\n",
      " -0.37265468  1.0623765  -0.40876797 -0.29349878  1.66605365 -0.60800314\n",
      " -0.71132022 -0.76389921 -0.68214482 -0.75236422 -0.51895678 -0.68279648\n",
      " -0.63774228 -0.21402133 -0.69347054 -0.56391251 -0.5642398  -0.45661059\n",
      " -0.06873369 -0.64450419 -0.45951104  0.41641283 -0.7045545  -0.61198163\n",
      " -0.6443615  -0.67953765 -0.41406462 -0.64292872 -0.94153875 -1.03704071\n",
      " -0.95639724 -0.89838362 -0.74032831 -1.11717343 -0.78222561 -0.85688478\n",
      " -0.8063134  -0.88994646 -0.38940078 -0.22627288 -0.79449105  0.08353227\n",
      " -0.05280876  0.24121177  0.85721195]\n",
      "================\n",
      "Epoch 10000\n",
      "Loss: [ 3.45317602  1.35866916  0.49156302  0.07451394 -0.45275211 -0.44502598\n",
      " -0.3005603   0.31900483 -0.32338285 -0.24405482 -0.47236267 -0.09179813\n",
      "  0.41166443  0.3479073   0.86230898 -0.4511503   0.36333799  2.56167746\n",
      " -0.35728589 -0.48606032  0.33883452 -0.19265783 -0.44711399 -0.54267532\n",
      " -0.39058179  0.06963634 -0.23751578 -0.54102641 -0.47965333 -0.52228767\n",
      " -0.11404011  0.71053284  0.50902462  0.13507849  0.1055606   0.20811611\n",
      " -0.25322822  0.50228107  0.21719116 -0.0623289  -0.48352608  0.08422679\n",
      " -0.52458954 -0.17142758 -0.50408095 -0.45655286 -0.45992899 -0.41671693\n",
      " -0.4674817   0.42902428 -0.45763844 -0.43592769 -0.20040095  0.44633299\n",
      "  0.60399586  0.14147782 -0.39836115 -0.29616213 -0.52795529 -0.83440071\n",
      " -0.81774098 -0.8627699  -0.70070475 -0.79577589 -0.32547581 -0.82604367\n",
      " -0.78594029 -0.53510427 -0.74170327 -0.61198586 -0.56645167 -0.70915037\n",
      " -0.65008426 -0.65146405 -0.63886857 -0.61829054 -0.54636031 -0.7756418\n",
      " -0.62054157 -0.99580383 -0.88937956 -0.57291704  3.79858899 -0.89473939\n",
      " -0.86896068 -0.82854432 -1.2468363  -0.91910303 -0.73063111 -0.56857407\n",
      " -0.52717704 -0.6155504  -0.66897547 -0.84171826 -1.0237875  -0.20719516\n",
      " -0.7377187   2.17580557  1.76833272]\n",
      "================\n",
      "Epoch 12000\n",
      "Loss: [  3.60177732e+00   1.11030877e-01   8.15570176e-01  -4.40326869e-01\n",
      "  -4.33261573e-01  -3.44801605e-01  -4.97503042e-01  -2.85971522e-01\n",
      "  -4.99804169e-01   2.44611979e-01   4.41377938e-01  -4.43739653e-01\n",
      "  -1.82743579e-01  -2.87834406e-02   2.76189029e-01  -2.95470864e-01\n",
      "   1.75515771e-01  -2.01531947e-01  -3.48775625e-01  -4.28826869e-01\n",
      "  -2.99322605e-01  -4.97238874e-01  -5.62755585e-01  -5.15806377e-01\n",
      "  -4.42273408e-01  -5.07188082e-01  -1.15787864e-01  -5.05801976e-01\n",
      "  -4.48163331e-01  -2.73710191e-01  -5.17722070e-01  -3.18388104e-01\n",
      "   1.43916488e-01  -5.92617750e-01   1.31481886e-03   9.44981933e-01\n",
      "   1.69310331e-01   1.73272014e-01   1.71519518e-01  -3.39614362e-01\n",
      "  -2.30637580e-01  -1.96332872e-01  -5.86074710e-01  -5.25561094e-01\n",
      "  -5.19192576e-01  -1.90241545e-01  -5.10359347e-01   9.63991880e-03\n",
      "  -3.72043818e-01   1.15537298e+00  -1.76289797e-01   2.95671821e-02\n",
      "  -5.66564620e-01  -2.18495488e-01  -4.10465002e-01  -8.54539275e-02\n",
      "  -2.01905847e-01   7.01591492e-01  -2.64971256e-02  -4.37395573e-01\n",
      "  -8.70772660e-01  -8.17017436e-01  -1.36598158e+00  -9.23488081e-01\n",
      "  -7.00517774e-01  -9.58959281e-01  -9.00406957e-01  -7.64869034e-01\n",
      "  -7.22311199e-01  -7.31938362e-01  -7.68637300e-01  -6.04152203e-01\n",
      "  -7.63212144e-01  -6.50462270e-01  -8.20194662e-01  -5.11230230e-01\n",
      "  -5.21743298e-01  -8.18343282e-01  -1.10272396e+00  -7.55492687e-01\n",
      "  -1.10537314e+00  -1.03939879e+00  -1.06333506e+00  -1.10474384e+00\n",
      "  -1.21073437e+00  -1.16582692e+00  -1.11877465e+00  -1.38156474e+00\n",
      "  -5.44461846e-01  -1.23727262e+00  -5.84615111e-01  -1.19628906e+00\n",
      "  -3.84149134e-01  -6.27792299e-01  -1.04828715e-01   1.27161145e+00\n",
      "   1.98365355e+00  -1.47940278e+00   1.06203032e+00]\n",
      "================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14000\n",
      "Loss: [  3.88772845e+00   1.83875799e-01   1.46208024e+00  -5.27171969e-01\n",
      "  -4.65776801e-01  -5.00928402e-01   1.24012876e+00   1.95333362e+00\n",
      "  -4.94642049e-01  -2.41094828e-02  -4.20169473e-01  -1.36318326e-01\n",
      "   4.68244553e-01  -9.89377201e-02  -3.52409512e-01  -4.51150447e-01\n",
      "   2.36027777e-01  -2.21285313e-01  -8.35978985e-03   4.04581189e-01\n",
      "  -4.40257043e-01   5.75278580e-01  -4.98449981e-01  -5.38711727e-01\n",
      "   2.11799800e-01  -1.28079981e-01   3.23357761e-01  -4.35802221e-01\n",
      "  -4.78997290e-01  -5.32975435e-01  -6.30353570e-01  -1.72270209e-01\n",
      "   4.99164701e-01  -4.64905500e-02  -4.85018492e-01  -6.76670074e-02\n",
      "  -3.36205602e-01  -3.50190103e-01   6.47120476e-01  -6.16743684e-01\n",
      "  -1.15680695e-02   1.43733621e-03  -6.19159698e-01  -4.85397428e-01\n",
      "  -5.91627002e-01  -5.13686240e-01  -5.62512040e-01  -5.62147379e-01\n",
      "  -4.88710582e-01  -2.86979198e-01   4.03685331e-01   3.90111566e-01\n",
      "  -4.42881942e-01  -3.79635096e-01  -2.08755612e-01  -2.62095898e-01\n",
      "  -2.12489754e-01  -5.25912285e-01   2.03569531e-02  -8.30213547e-01\n",
      "  -9.66181517e-01  -1.17128944e+00  -9.15915847e-01  -1.00230622e+00\n",
      "  -1.01457262e+00  -1.02234173e+00  -1.14002490e+00  -7.15939820e-01\n",
      "  -9.33057189e-01  -7.76566625e-01  -8.64869237e-01  -6.42920256e-01\n",
      "  -9.27842379e-01  -6.93987489e-01  -7.59806633e-01   2.20528078e+00\n",
      "  -9.39120173e-01  -5.79940856e-01  -8.72529864e-01  -9.58733439e-01\n",
      "  -1.01759744e+00  -1.33619499e+00  -8.36404264e-01  -1.17203391e+00\n",
      "  -7.51387775e-01  -1.19738340e+00  -9.88713145e-01  -1.01309121e+00\n",
      "  -1.15557766e+00  -1.20393610e+00  -6.06034040e-01  -5.88280499e-01\n",
      "  -8.43974710e-01  -8.43223989e-01  -9.67084646e-01  -3.47128987e-01\n",
      "   2.39737868e-01   1.16226411e+00   6.21618629e-01]\n",
      "================\n",
      "Epoch 16000\n",
      "Loss: [ 2.98355103  0.05407995  2.50184059 -0.57683784 -0.48057652 -0.56751686\n",
      "  0.08801597  0.42976838  0.17024034  0.88653469 -0.22350317  0.55326408\n",
      "  0.23067665  0.62344664  0.99477232 -0.42111817  0.59033549  0.21544051\n",
      " -0.05817562 -0.57176232 -0.29908878 -0.34749234 -0.60549486 -0.53319228\n",
      " -0.18767175 -0.46102309 -0.38025984 -0.61309201 -0.45816883 -0.1785551\n",
      "  0.1181317  -0.39232391  0.15968686 -0.46228218 -0.70031548 -0.17998776\n",
      " -0.10078478 -0.61411184 -0.1179392  -0.59699452  0.3320412  -0.26562795\n",
      " -0.53473151 -0.56771755 -0.59837592 -0.58178455  0.15014011 -0.11892483\n",
      " -0.36504716  0.29821414  1.08455753 -0.45033383 -0.56424856 -0.44620857\n",
      " -0.43889791 -0.44736579 -0.42553955  0.63135773 -0.01538882 -0.99473149\n",
      " -0.92868435 -1.1138773  -1.05158794 -1.00438213 -0.77693492 -0.99108046\n",
      " -0.95817381 -0.83102876 -0.8823244  -0.96373856 -0.8241936  -0.93090165\n",
      " -0.95129263 -1.04763496 -0.7991007  -0.84399295 -0.91685861 -1.23374212\n",
      " -0.90918362 -0.85587656 -1.08946323 -0.57191938 -1.27984715 -1.18105316\n",
      " -1.19584095 -0.70096481 -1.14564526 -0.86009121 -1.23840213 -1.33187938\n",
      " -1.15755343 -1.16941285 -1.21800649 -1.64583254 -1.21385717 -1.58648825\n",
      " -1.17978954 -1.4291631   1.94377398]\n",
      "================\n",
      "Epoch 18000\n",
      "Loss: [ 2.88912559  0.04470164  1.4837054  -0.55657446 -0.03919834 -0.55103332\n",
      " -0.21589884  1.65002775 -0.44736776 -0.01268047  0.55763984 -0.0722785\n",
      " -0.33163387  1.18802702  0.97829914 -0.2336798  -0.1938858   0.01171094\n",
      " -0.49740994 -0.33334231 -0.39052474 -0.51434457 -0.46945086 -0.58557749\n",
      " -0.44839042  0.1508463  -0.68487018 -0.48999137 -0.55156535 -0.31889984\n",
      " -0.29540756  0.22064906  0.03281814  0.34206372  0.52046591  0.85104811\n",
      "  0.33710301  0.11001879  0.99572492 -0.52671033 -0.02971172 -0.19343087\n",
      " -0.57571125  0.66566336 -0.55426466 -0.32393089 -0.39410171  0.27492309\n",
      "  0.40098733  0.88472998  2.63058662 -0.5508883   0.30208564 -0.13911775\n",
      "  0.41481221  0.59569943  0.60471463  0.63461852  0.03885448 -1.13351941\n",
      " -1.23823988 -1.27293599 -1.2122618  -1.28070939 -1.27856874 -0.92597657\n",
      " -0.96120119 -1.17259121 -0.89588934 -1.15116608 -0.82279706 -0.90506411\n",
      " -0.92580694 -0.81371802 -0.75332749 -0.76309848 -0.74035943 -1.25244486\n",
      " -0.86430085 -1.44233775 -1.17645597 -1.2392664  -1.44585478 -1.18200123\n",
      " -1.41129422 -0.86265308 -1.66050363 -1.16257536 -1.09443402 -1.21335673\n",
      " -0.56587648 -0.47239709 -0.86686552  0.01609766 -1.25747097 -1.42762864\n",
      " -1.42742372 -0.33033687  0.34383488]\n",
      "================\n",
      "Epoch 20000\n",
      "Loss: [ 4.06718874  0.1763885   1.05072153 -0.46877575 -0.51070285 -0.52308291\n",
      " -0.28421476  0.29125887 -0.42248026 -0.25694799 -0.19504979 -0.46629089\n",
      " -0.27246889  0.08610928  0.19664711 -0.52835304 -0.41813636  0.42614186\n",
      "  0.27302945 -0.44725028  0.07094359 -0.48987204 -0.43941969 -0.49747163\n",
      " -0.48028627 -0.52431595 -0.46789175 -0.52612448 -0.4604494  -0.50712729\n",
      " -0.51179552 -0.56429952 -0.22054464  0.45912069  0.21931052 -0.10370833\n",
      "  0.21608853 -0.50076884  0.78765178 -0.40120596  0.17011434 -0.523377\n",
      " -0.03748018 -0.53543228 -0.52846289 -0.51162034 -0.51722199  0.8458162\n",
      " -0.50255227  0.41080481  0.93112153 -0.50883776 -0.46717775 -0.01118416\n",
      "  4.40128231  0.49250352 -0.02391919  0.55250925 -0.45861238 -1.01677477\n",
      " -1.15328801 -1.36843312 -1.09613097 -1.43833911 -0.88944477 -1.28227913\n",
      " -1.12438321 -1.11904478 -1.28615963 -1.05686116 -1.03764546 -0.93809712\n",
      " -0.82573032 -0.88601387 -1.04009926 -1.05164051 -0.96681058 -1.11460257\n",
      " -1.12960458 -1.49157619 -1.25873995 -1.32137144 -1.20741951 -1.33181632\n",
      " -1.49915481 -1.43613374 -0.98099864 -1.4505893  -1.58872509 -0.63514531\n",
      " -0.95286131 -1.3055861  -0.10319555  0.75636482 -0.70388675  0.47001958\n",
      " -0.23969221 -1.48997092  2.21039867]\n",
      "================\n",
      "Epoch 22000\n",
      "Loss: [  2.00880480e+00   4.45054233e-01   6.58946514e-01  -3.00896794e-01\n",
      "  -5.83899617e-01  -5.84634960e-01  -3.52194399e-01   1.44944787e-02\n",
      "  -4.52151060e-01   3.98913741e-01  -3.66990030e-01   1.37779701e+00\n",
      "  -4.93342578e-02  -1.11864805e-02  -2.78419316e-01   8.17835331e-04\n",
      "  -8.10910165e-02   1.41854465e-01  -1.86827958e-01  -4.34013963e-01\n",
      "  -1.39293075e-02  -3.29811662e-01  -6.27862394e-01  -3.00218582e-01\n",
      "  -1.63642794e-01  -6.59411073e-01  -6.27955019e-01  -5.40899873e-01\n",
      "  -1.25368655e-01   5.81208467e-02  -3.84414196e-01  -5.04972696e-01\n",
      "  -4.54485685e-01   6.84369445e-01  -2.22182721e-01   4.25732315e-01\n",
      "  -1.88454539e-01  -8.63131285e-02  -2.96601772e-01  -6.18363798e-01\n",
      "  -4.29524392e-01  -5.42367816e-01  -2.71296859e-01  -6.29040301e-01\n",
      "  -1.95880502e-01  -6.11010909e-01  -6.28801644e-01   1.73819661e+00\n",
      "  -2.79807091e-01   6.70175850e-02  -3.58958185e-01  -5.15093863e-01\n",
      "   9.90547717e-01   1.48228097e+00   2.53527701e-01  -6.23405933e-01\n",
      "   1.73322022e-01   3.38733959e+00  -8.93356502e-02  -1.21994865e+00\n",
      "  -1.29151392e+00  -1.43607950e+00  -1.41641188e+00  -1.09726596e+00\n",
      "  -1.09144545e+00  -1.14237452e+00  -9.27534401e-01  -1.06960928e+00\n",
      "  -1.11030304e+00  -1.01575232e+00  -1.23646212e+00  -1.35539579e+00\n",
      "  -1.14551449e+00  -1.18417501e+00  -1.05752409e+00  -1.02442491e+00\n",
      "  -1.15417075e+00  -1.38939452e+00  -1.33026075e+00  -1.11626995e+00\n",
      "  -1.27879429e+00  -1.71991527e+00  -1.21704948e+00  -1.32014418e+00\n",
      "  -1.49180365e+00  -1.70881891e+00  -1.60834730e+00  -1.52378285e+00\n",
      "  -1.41203713e+00  -1.25938892e+00   4.10565734e-01  -1.71772492e+00\n",
      "  -6.28443539e-01   1.23474956e-01  -1.16508007e+00   1.15129137e+01\n",
      "  -6.44153535e-01  -1.86900270e+00  -9.91196156e-01]\n",
      "================\n",
      "Epoch 24000\n",
      "Loss: [ 4.40806961  0.63377917  1.01079476 -0.37487057 -0.30008006 -0.5002315\n",
      "  0.0612787   0.05266547 -0.31516385  0.05905992 -0.33156252  0.0655756\n",
      " -0.08283883 -0.27754998  0.08917725 -0.3483803   1.17670441  0.03476954\n",
      "  0.48431426 -0.47103414  0.10972339 -0.16964182 -0.73679471 -0.63882661\n",
      " -0.39094669 -0.47106504 -0.39912656 -0.35475844 -0.27761826 -0.480138\n",
      " -0.19948611 -0.68741274  0.43602377 -0.68838686 -0.24615473  0.11596525\n",
      " -0.4079158  -0.56964839  0.85794967 -0.41386583  0.11445123 -0.28405696\n",
      " -0.48508042 -0.38106641 -0.23450851 -0.40010935 -0.58849907  0.07355076\n",
      "  0.08504617  2.13503456  0.38239956 -0.36916333  0.13338521 -0.50784659\n",
      "  0.51670742  0.40245891  0.07224584  0.36975282  0.12816072 -1.30098891\n",
      " -1.3239007  -1.45065784 -1.38830459 -1.48887157 -1.11412156 -1.39082158\n",
      " -1.05371368 -1.02678847 -1.31058323 -1.10460353 -1.27939987 -1.0159173\n",
      " -1.0959872  -1.10980177 -0.99649882 -1.38940573 -1.18235815 -1.19285595\n",
      " -1.67336226 -1.41769755 -1.26059318 -1.7448653  -1.41144741 -1.69260359\n",
      " -1.31809402 -1.31164324 -1.58337045 -1.34656227 -1.27713013 -0.68152517\n",
      " -0.83761609 -1.63433123 -1.6963594  -0.32891417 -1.2927978  -1.52155447\n",
      "  7.82829046  1.81607831 -1.46241522]\n",
      "================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26000\n",
      "Loss: [ 3.71521473  0.10997754  0.40931815 -0.15140122 -0.57162225 -0.55694157\n",
      "  0.08269674  0.81218159 -0.34265029 -0.01552296  0.77288127 -0.24725358\n",
      "  2.10144043 -0.03330457  0.87812448 -0.06277621  0.22409326  0.59388602\n",
      " -0.15706901 -0.38161784 -0.18643138 -0.56884891 -0.55027354 -0.33432913\n",
      "  0.26148689 -0.60316187 -0.11263016 -0.52262783 -0.58446246  0.78488827\n",
      "  0.0649907  -0.19273636  1.50688624  0.65310812  0.91801089 -0.03730702\n",
      " -0.11802152 -0.4154585   0.44275481 -0.6465801  -0.29224503 -0.6309489\n",
      " -0.70552862 -0.56399906 -0.16382629 -0.54498792 -0.4433699   0.56367087\n",
      " -0.37369952 -0.31454036 -0.37319905 -0.56593764 -0.36038139 -0.07756948\n",
      "  0.17785376 -0.38493913 -0.38006777  0.22555661  0.06679571 -1.23646116\n",
      " -1.38600481 -1.58658803 -1.59976125 -1.38103127 -1.8373208  -1.2552985\n",
      " -1.23235476 -1.42152643 -1.63141251 -1.84258807 -1.29668558 -1.45000708\n",
      " -1.48173547 -1.23544395 -1.57157958 -1.47509837 -1.07946754 -1.75469851\n",
      " -1.54519391 -1.75697148 -1.62976968 -1.59998882 -1.4882251  -1.41609764\n",
      " -1.72222805 -1.49039173 -1.74770653 -1.36814582 -1.31269479 -1.54273772\n",
      "  3.00920057 -0.97324574  0.15734124 -1.52030241  3.00709534 -1.44528532\n",
      " -1.24656987  5.21920395  3.30237341]\n",
      "================\n",
      "Epoch 28000\n",
      "Loss: [  4.04128170e+00   4.42001581e-01   1.70916295e+00  -5.91935754e-01\n",
      "  -4.55437124e-01  -8.09199989e-01   2.42516458e-01   4.73095417e-01\n",
      "  -3.57560903e-01  -1.03028715e-01   2.44956923e+00  -2.24083781e-01\n",
      "   4.59552288e-01   5.09561300e-01  -3.14821184e-01  -2.67609149e-01\n",
      "   5.18788099e-01  -3.85740310e-01  -4.73636001e-01  -5.16696811e-01\n",
      "   3.66526842e-03  -2.67931253e-01  -6.43724680e-01   5.11148870e-02\n",
      "  -2.53760546e-01  -3.84532779e-01  -5.61795056e-01  -6.01010680e-01\n",
      "  -6.33185387e-01  -1.21772408e-01  -4.17134643e-01  -4.25923347e-01\n",
      "  -4.37738508e-01  -2.45179832e-01  -2.56161004e-01   3.88465524e-01\n",
      "   5.04654467e-01  -7.04884887e-01   2.07313657e-01  -4.73820359e-01\n",
      "   2.93302059e-01  -4.85715032e-01  -3.69387001e-01  -3.22277963e-01\n",
      "  -4.15323377e-01  -3.17928940e-01  -5.50950110e-01  -2.60720789e-01\n",
      "  -5.07820010e-01  -2.59694904e-01   2.05857754e-02  -3.50538701e-01\n",
      "   1.15988874e+00  -2.56653458e-01   7.51069486e-01  -3.31348181e-03\n",
      "  -4.29914862e-01   5.26197076e-01   9.43867564e-01  -1.18184173e+00\n",
      "  -1.44609213e+00  -1.76343834e+00  -1.80643082e+00  -1.64340258e+00\n",
      "  -1.28593004e+00  -1.34568644e+00  -1.21969795e+00  -1.43464351e+00\n",
      "  -1.24151731e+00  -1.14132881e+00  -1.25527668e+00  -1.07146382e+00\n",
      "  -1.26597440e+00  -8.48228395e-01  -1.27279663e+00  -1.29748785e+00\n",
      "  -1.10335386e+00  -2.06587052e+00  -1.09544408e+00  -1.27401745e+00\n",
      "  -1.31233311e+00  -1.53019118e+00  -1.37776387e+00  -1.23637009e+00\n",
      "  -1.17900896e+00  -1.12076771e+00   2.54568529e+00  -1.61194193e+00\n",
      "   2.49978113e+00  -6.25399768e-01   3.85856318e+00  -1.01954961e+00\n",
      "  -1.44392681e+00  -1.48600876e+00  -1.08385134e+00   1.95108175e-01\n",
      "  -4.60186005e-01  -1.00027573e+00  -3.13252330e-01]\n",
      "================\n",
      "Epoch 30000\n",
      "Loss: [ 3.27601242 -0.01402658  1.10354888 -0.39237961 -0.457362   -0.57473361\n",
      " -0.31178203  1.66520452  0.35525119 -0.25688297 -0.39440975 -0.38515529\n",
      "  0.48685849 -0.19991896 -0.33319771 -0.14249265  0.67900145  0.30061811\n",
      " -0.13283947 -0.35876444 -0.37482685 -0.30448651 -0.52333927 -0.66473389\n",
      "  0.11029249 -0.58217925 -0.52978122 -0.47777984 -0.29430738 -0.24816941\n",
      " -0.48051146 -0.63218176  0.40383232 -0.45959091  0.61363757  0.51511014\n",
      "  0.24329108 -0.65518987 -0.69553322 -0.60996532 -0.18100305  0.13057196\n",
      " -0.39558789 -0.47860032 -0.15941244 -0.35207814 -0.47097376  0.94530582\n",
      " -0.0740231  -0.31235668 -0.64513481 -0.34937432 -0.5725112  -0.34176648\n",
      "  0.48455524 -0.14364702 -0.34606457  1.30032754 -0.29663098 -1.11047387\n",
      " -1.20950794 -1.53283703 -1.69025552 -1.59302235 -1.36454701 -1.40808904\n",
      " -1.49706805 -1.54663038 -1.32854772 -1.45336449 -1.2857945  -1.63351071\n",
      " -1.65561616 -1.55995691 -1.2685796  -1.33685732 -1.70841837 -1.92266333\n",
      " -1.62413859 -1.96980238 -1.93366826 -2.01278138 -1.62358427 -1.74408269\n",
      " -1.70598769 -1.25879729 -1.57567918 -1.49213123 -1.52591789 -1.45352387\n",
      " -1.55241954 -0.92277986  0.22669733 -0.27214563 -0.54948884 -0.3095839\n",
      "  0.52895355 -0.5492304  -0.18886161]\n",
      "================\n",
      "Epoch 32000\n",
      "Loss: [ 5.82517815  0.1998415   1.11219466 -0.60449171  0.36531079 -0.67913401\n",
      "  0.27801931  1.18309104 -0.58334023  0.57013416 -0.06095734 -0.44347334\n",
      " -0.12260547  1.57420897 -0.28144446 -0.14471677 -0.32029864 -0.11978137\n",
      "  0.4975338  -0.36599481  0.79536134 -0.08776784 -0.62757224 -0.49054307\n",
      " -0.36087739 -0.4934656  -0.74139196 -0.30989733 -0.54247177  0.05506581\n",
      " -0.62165266 -0.47399926  0.69736993 -0.45522711  0.27190077 -0.9177348\n",
      "  0.18569863 -0.57604295  0.38301319 -0.25024766 -0.52834362 -0.57960182\n",
      " -0.6285131  -0.6074844  -0.34579331 -0.39764389  0.02287024  0.07806724\n",
      " -0.56908917 -0.31902993 -0.43014359 -0.47526649  0.03052574 -0.42643321\n",
      " -0.32916254 -0.42498416  0.45156771  1.3194679  -0.00635719 -1.22110891\n",
      " -1.22525477 -1.70226002 -1.55082393 -1.78622246 -1.47638869 -1.56015527\n",
      " -1.76990271 -1.98693836 -1.41573238 -1.12718368 -1.66347313 -0.97615153\n",
      " -2.10744047 -1.2325542  -1.46687615 -1.78512239 -1.39219439 -1.64562094\n",
      " -1.86563635 -1.85975206 -1.61082888 -1.80153072 -1.48940194 -1.58461869\n",
      " -1.22939849 -1.42169023 -1.98456657 -0.9575491  -1.72100937 -1.59323025\n",
      " -1.23401666 -1.0832845  -0.81756651 -0.45189989 -1.14065087 -0.93772793\n",
      "  1.88786936 -1.37716269 -0.8232559 ]\n",
      "================\n",
      "Epoch 34000\n",
      "Loss: [ 3.53845763  0.33129358  0.9017663  -0.01598093 -0.34431183 -0.44492882\n",
      "  0.13810712  0.19779426 -0.370866    0.39157915 -0.32574105  2.10407877\n",
      "  0.62498581 -0.15012586 -0.17315578 -0.13966054 -0.17607081 -0.25718689\n",
      " -0.47462368 -0.42111558 -0.45783192 -0.3737748  -0.3560431  -0.43265951\n",
      " -0.47455627 -0.30172282 -0.38945973 -0.35301876 -0.35303581 -0.03549862\n",
      " -0.52861267 -0.7880491   0.19764572  0.29262513  0.01534066  0.38820124\n",
      "  0.59331107 -0.08470958  0.19648516 -0.37539375 -0.05784082 -0.59954411\n",
      " -0.54902524 -0.69569474  0.34803879  0.64206141 -0.3518866  -0.30702484\n",
      "  1.1568197  -0.3290945   3.56445336  0.89309394  0.46358043 -0.30482838\n",
      " -0.14091074 -0.49231562  0.32074344 -0.27709404 -0.59326231 -1.22135139\n",
      " -1.5648694  -1.63320732 -1.73454177 -1.44878972 -2.04913998 -1.9339329\n",
      " -1.99898398 -1.7421031  -1.30744696 -1.38299012 -0.89454377 -1.46184897\n",
      " -1.94302261 -1.68133295 -1.29067671 -1.43823409 -1.58839476 -1.49105108\n",
      " -2.19328427 -1.63994169 -1.2699424  -1.45533729  0.88147831 -1.94335413\n",
      " -1.79902029 -1.65672565 -1.50302398 -1.66390288 -1.28412426 -1.01567554\n",
      " -1.45294881 -1.23327255 -1.50206232 -0.73194098 -1.0221194  -1.15409708\n",
      "  1.28889239 -1.40322793 -1.08937192]\n",
      "================\n",
      "Epoch 36000\n",
      "Loss: [ 3.17292953  0.52005672  0.38733494 -0.57299465 -0.46239167 -0.50539464\n",
      "  0.08611596  0.34619677 -0.51658356 -0.24362624 -0.33688951 -0.30151802\n",
      "  0.73964429  0.19405806 -0.11934859  0.01794809  0.23954791 -0.15112987\n",
      " -0.23953241 -0.26397693  0.48871386 -0.26489258 -0.54981518 -0.36423197\n",
      "  0.07973307 -0.03630468  0.02366868 -0.58763814  0.09243095 -0.11375174\n",
      " -0.54511559 -0.10777381  0.63514131 -0.4797492  -0.220267   -0.51819527\n",
      " -0.82071573 -0.61289585  0.27745509 -0.55209798 -0.21342283 -0.12986118\n",
      " -0.35938907 -0.4869777  -0.78905952 -0.25952157 -0.42823857 -0.44884777\n",
      "  0.14508408 -0.53447366  0.07935041  0.03408188  0.31850964 -0.40902612\n",
      " -0.38895571 -0.32694989 -0.1871998   0.03053153  0.94296426 -1.37739635\n",
      " -1.59013367 -0.49515843 -1.75282431 -1.56304216 -1.81736922 -1.3788054\n",
      " -1.57943952 -1.51600277 -1.3380475  -1.68319273 -1.73802233 -1.66754389\n",
      " -1.07991552 -1.55709612 -1.34321892 -1.61823964 -1.54149032 -1.94294512\n",
      " -2.35811996 -2.13441157 -2.03401041 -2.07105947  0.35841203 -1.36776757\n",
      " -0.02130342 -1.80065143 -1.22937524 -1.30708659 -1.40714288 -1.47367275\n",
      " -1.55142105 -0.00736868 -0.41543132 -0.77705753 -1.33843756 -0.35732949\n",
      " -0.31223583 -0.8437807  -0.22761875]\n",
      "================\n",
      "Epoch 38000\n",
      "Loss: [ 2.87455249  0.51795626  0.75868052 -0.73375577 -0.45501569 -0.70515043\n",
      " -0.10850686  0.55790794 -0.33065158 -0.25651628 -0.27481025 -0.39762259\n",
      " -0.29819894 -0.13961542  0.21392369 -0.46231878  0.45356536 -0.11621496\n",
      " -0.24524006 -0.48340541 -0.27669314 -0.28069174 -0.54738468 -0.49262139\n",
      " -0.40435201 -0.18853256 -0.7177701  -0.72531188 -0.26164174 -0.49582905\n",
      " -0.06407478 -0.16356412 -0.84642142 -0.77819121 -0.58658332  0.43748236\n",
      "  0.93085527 -0.24311447  0.29820645 -0.75427347  0.18128413 -0.15081201\n",
      " -0.37797773 -0.25892454 -0.05396473 -0.20019843 -0.51364893  2.76556993\n",
      " -0.49174452  0.40340626 -0.21747696 -0.13433352 -0.0131672   0.32756549\n",
      "  2.02469134  4.53816128  0.08302778  0.53455222 -0.34217417 -0.7776258\n",
      " -1.55986512 -1.34035814 -1.66132379 -1.47030282 -1.8011626  -1.7064563\n",
      " -1.43453157 -1.51491463 -1.47579527 -1.92875826 -2.00024462 -1.72834659\n",
      " -1.33064473 -1.21056569 -1.51887488 -1.78820002 -2.16713119 -2.17194319\n",
      " -1.41797662 -2.10354018 -2.02568364 -1.53716731 -2.17383933 -2.04280972\n",
      " -1.52460515 -1.83031797 -1.69587743 -0.49973339 -1.56311655 -1.56627846\n",
      " -1.46561944  1.29237032  0.31985283 -1.33930039 -0.39673543 -0.99700874\n",
      " -1.15572619 -1.01171064 -0.94673473]\n",
      "================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training done\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (230, 1) and (140, 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-385-1358fb6646c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     97\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_y_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Predictive Mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Aleatoric Variance\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_epistemic_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Epistemic Variance\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;31m#plt.plot(pred_x, combined_uncertainty, label=\"Combined Uncertainty\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/disk/users/hutmache/.Envs/ros_e2e/local/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3238\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3239\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3240\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3241\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3242\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/disk/users/hutmache/.Envs/ros_e2e/local/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1708\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1709\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1710\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1711\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1712\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/disk/users/hutmache/.Envs/ros_e2e/local/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1435\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1437\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1438\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1439\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/disk/users/hutmache/.Envs/ros_e2e/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/disk/users/hutmache/.Envs/ros_e2e/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    382\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/disk/users/hutmache/.Envs/ros_e2e/local/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 243\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (230, 1) and (140, 1)"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAJCCAYAAACxsxylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa31ddfbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_passes = 30\n",
    "dropout_rate = 0.4\n",
    "\n",
    "n_samples = 100\n",
    "x, y, w = generate_samples_heteroscedastic(n_samples, variance=0.03)\n",
    "#x, y = generate_nonlinear()\n",
    "\n",
    "if 0 in x:\n",
    "    x = np.delete(x, 0)\n",
    "    y = np.delete(y, 0)\n",
    "\n",
    "x = x.reshape([-1, 1])\n",
    "y = y.reshape([-1, 1])\n",
    "\n",
    "\n",
    "x_data = tf.placeholder(tf.float32, [None, 1], name=\"x_data\")\n",
    "y_data = tf.placeholder(tf.float32, [None, 1], name=\"y_data\")\n",
    "\n",
    "def combined_network(x):\n",
    "    with tf.device(\"/gpu:0\"):\n",
    "        training= True\n",
    "            \n",
    "        fc1 = tf.layers.dense(inputs=x, units=50, activation=tf.nn.relu)\n",
    "        fc1 = tf.layers.dropout(fc1, dropout_rate, training=True)\n",
    "        \n",
    "        fc2 = tf.layers.dense(inputs=fc1, units=50, activation=tf.nn.relu)\n",
    "        fc2 = tf.layers.dropout(fc2, dropout_rate, training=True)\n",
    "        \n",
    "\n",
    "        # Output layers has predictive mean and variance sigma^2\n",
    "        output_layer = tf.layers.dense(fc2, units=2)\n",
    "        predictions = tf.reshape(output_layer, [-1, 2])\n",
    "        \n",
    "        return predictions\n",
    "\n",
    "predictions = combined_network(x_data)\n",
    "y_hat = tf.reshape(predictions[:, 0], [-1, 1]) \n",
    "s = tf.reshape(predictions[:, 1], [-1, 1])\n",
    "#sigma2 = tf.reshape(predictions[:, 1], [-1, 1]) \n",
    "#sigma2 = tf.square(sigma2)\n",
    "\n",
    "learning_rate = 0.0001\n",
    "display_step = 2000\n",
    "training_epochs = 40000\n",
    "\n",
    "#optimizer = tf.train.AdamOptimizer(learning_rate)\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
    "#optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9, use_nesterov=True)\n",
    "\n",
    "# Loss for combined aleatoric and epistemic uncertainty\n",
    "# TODO: Add weight decay\n",
    "#s = tf.log(sigma2)\n",
    "combined_loss = tf.reduce_sum(0.5 * tf.exp(-1 * s) * tf.square(tf.abs(y_data - y_hat)) + 0.5 * s, axis=1)\n",
    "#mbined_loss = (tf.nn.l2_loss(y_data - y_hat) / 2*sigma2) + tf.log(sigma2)\n",
    "\n",
    "train = optimizer.minimize(combined_loss) \n",
    "\n",
    "init = tf.global_variables_initializer()                                  \n",
    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "sess.run(init)\n",
    "\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    sess.run(train, feed_dict={x_data: x, y_data: y})\n",
    "\n",
    "    if (epoch % display_step == 0):\n",
    "        print(\"Epoch {}\".format(epoch))\n",
    "        loss = sess.run(combined_loss, feed_dict={x_data: x,\n",
    "                                                  y_data: y})\n",
    "        print(\"Loss: {}\".format(loss))\n",
    "        print(\"================\")\n",
    "\n",
    "print(\"Training done\")\n",
    "\n",
    "\n",
    "n_passes = 100\n",
    "pred_x = np.arange(-1.1, 1.2, 0.01)\n",
    "pred_x_multipass = np.array([[e] * n_passes for e in pred_x]).reshape([-1, 1])\n",
    "pred_x = pred_x.reshape([-1, 1])\n",
    "pred_y = sess.run(y_hat, feed_dict={x_data: pred_x_multipass})\n",
    "#sigma2_multipass = sess.run(sigma2, feed_dict={x_data: pred_x_multipass})\n",
    "pred_y = pred_y.reshape(-1, n_passes)\n",
    "#sigma2_multipass = sigma2_multipass.reshape(-1, n_passes)\n",
    "#sigma2_mean = sigma2_multipass.reshape(-1, n_passes).mean(axis=1).reshape(-1, 1)\n",
    "\n",
    "# pred_y has now multipass shape\n",
    "pred_y_mean = pred_y.mean(axis=1).reshape([-1, 1])\n",
    "pred_y_mean_squared = np.square(pred_y).mean(axis=1).reshape([-1, 1])\n",
    "\n",
    "\n",
    "pred_epistemic_var = pred_y.var(axis=1).reshape([-1, 1])\n",
    "ic_var = pred_y.std(axis=1).reshape([-1, 1])\n",
    "#pred_var = sess.run(sigma2, feed_dict={x_data: pred_x})\n",
    "\n",
    "#combined_uncertainty = pred_y_mean_squared - np.square(pred_y_mean) + sigma2_mean\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(pred_x, pred_y_mean, label=\"Predictive Mean\", linewidth=3)\n",
    "plt.plot(pred_x, pred_var, label=\"Aleatoric Variance\")\n",
    "plt.plot(pred_x, pred_epistemic_var, label=\"Epistemic Variance\")\n",
    "#plt.plot(pred_x, combined_uncertainty, label=\"Combined Uncertainty\")\n",
    "plt.scatter(x, y, label=\"training samples\", alpha=0.1)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffa31b181d0>"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAAJCCAYAAABNpjdvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAADQhqIRAAAAAAAA2vw/f3qB2t0kkloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa31f78c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_passes = 100\n",
    "pred_x = np.arange(-0.1, 1.2, 0.01)\n",
    "pred_x_multipass = np.array([[e] * n_passes for e in pred_x]).reshape([-1, 1])\n",
    "pred_x = pred_x.reshape([-1, 1])\n",
    "pred_y = sess.run(y_hat, feed_dict={x_data: pred_x_multipass})\n",
    "#sigma2_multipass = sess.run(sigma2, feed_dict={x_data: pred_x_multipass})\n",
    "pred_y = pred_y.reshape(-1, n_passes)\n",
    "#sigma2_multipass = sigma2_multipass.reshape(-1, n_passes)\n",
    "#sigma2_mean = sigma2_multipass.reshape(-1, n_passes).mean(axis=1).reshape(-1, 1)\n",
    "\n",
    "# pred_y has now multipass shape\n",
    "pred_y_mean = pred_y.mean(axis=1).reshape([-1, 1])\n",
    "pred_y_mean_squared = np.square(pred_y).mean(axis=1).reshape([-1, 1])\n",
    "\n",
    "\n",
    "pred_epistemic_var = pred_y.var(axis=1).reshape([-1, 1])\n",
    "ic_var = pred_y.std(axis=1).reshape([-1, 1])\n",
    "#pred_var = sess.run(sigma2, feed_dict={x_data: pred_x})\n",
    "\n",
    "#combined_uncertainty = pred_y_mean_squared - np.square(pred_y_mean) + sigma2_mean\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(pred_x, pred_y_mean, label=\"Predictive Mean\", linewidth=3)\n",
    "#plt.plot(pred_x, pred_var, label=\"Aleatoric Variance\")\n",
    "plt.plot(pred_x, prekkkkd_epistemic_var, label=\"Epistemic Variance\")\n",
    "#plt.plot(pred_x, combined_uncertainty, label=\"Combined Uncertainty\")\n",
    "plt.scatter(x, y, label=\"training samples\", alpha=0.1)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_multi = sess.run(s, feed_dict={x_data: pred_x_multipass})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13000, 1)"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  6.,  21.,  19.,  23.,  15.,   8.,   5.,   1.,   1.,   1.]),\n",
       " array([ 0.39016899,  0.7668069 ,  1.1434448 ,  1.5200827 ,  1.89672061,\n",
       "         2.27335851,  2.64999641,  3.02663431,  3.40327222,  3.77991012,\n",
       "         4.15654802]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa32fa9cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(pred_y.reshape(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffa32fdff10>"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAJCCAYAAACxsxylAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyvRPCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa3ba14450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_passes = 100\n",
    "#pred_x = np.arange(0.74, 0.75, 0.01)\n",
    "pred_x = np.array([0.65])\n",
    "pred_x_multipass = np.array([[e] * n_passes for e in pred_x]).reshape([-1, 1])\n",
    "pred_x = pred_x.reshape([-1, 1])\n",
    "pred_y = sess.run(y_hat, feed_dict={x_data: pred_x_multipass})\n",
    "pred_y = pred_y.reshape(-1, n_passes)\n",
    "\n",
    "# pred_y has now multipass shape\n",
    "pred_y_mean = pred_y.mean(axis=1).reshape([-1, 1])\n",
    "pred_y_mean_squared = np.square(pred_y).mean(axis=1).reshape([-1, 1])\n",
    "\n",
    "\n",
    "pred_epistemic_var = pred_y.var(axis=1).reshape([-1, 1])\n",
    "ic_var = pred_y.std(axis=1).reshape([-1, 1])\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(pred_x, pred_y_mean, label=\"Predictive Mean\", linewidth=3)\n",
    "#plt.plot(pred_x, pred_var, label=\"Aleatoric Variance\")\n",
    "plt.plot(pred_x, pred_epistemic_var, label=\"Epistemic Variance\")\n",
    "#plt.scatter(x, y, label=\"training samples\", alpha=0.8)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffa390e1350>"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAAJCCAYAAABNpjdvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa3a2f5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_passes = 100\n",
    "pred_x = np.arange(-0.2, 1.2, 0.01)\n",
    "pred_x_multipass = np.array([[e] * n_passes for e in pred_x]).reshape([-1, 1])\n",
    "pred_x = pred_x.reshape([-1, 1])\n",
    "pred_y = sess.run(y_hat, feed_dict={x_data: pred_x_multipass})\n",
    "sigma2_multipass = sess.run(sigma2, feed_dict={x_data: pred_x_multipass})\n",
    "pred_y = pred_y.reshape(-1, n_passes)lk\n",
    "sigma2_multipass = sigma2_multipass.reshape(-1, n_passes)\n",
    "sigma2_mean = sigma2_multipass.reshape(-1, n_passes).mean(axis=1).reshape(-1, 1)\n",
    "\n",
    "# pred_y has now multipass shape\n",
    "pred_y_mean = pred_y.mean(axis=1).reshape([-1, 1])\n",
    "pred_y_mean_squared = np.square(pred_y).mean(axis=1).reshape([-1, 1])\n",
    "\n",
    "\n",
    "pred_epistemic_var = pred_y.var(axis=1).reshape([-1, 1])\n",
    "ic_var = pred_y.std(axis=1).reshape([-1, 1])\n",
    "pred_var = sess.run(sigma2, feed_dict={x_data: pred_x})\n",
    "\n",
    "combined_uncertainty = pred_y_mean_squared - np.square(pred_y_mean) + sigma2_mean\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(pred_x, pred_y_mean, label=\"Predictive Mean\", linewidth=3)ö\n",
    "plt.plot(pred_x, pred_var, label=\"Aleatoric Variance\")\n",
    "plt.plot(pred_x, pred_epistemic_var, label=\"Epistemic Variance\")\n",
    "plt.plot(pred_x, combined_uncertainty, label=\"Combined Uncertainty\")\n",
    "plt.scatter(x, y, label=\"training samples\", alpha=0.8)\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ros_e2e",
   "language": "python",
   "name": "ros_e2e"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
